Customer Segmentation always remains a challenge in analyzing the market trends, where the businesses struggle to understand how the customer journeys and to generate optimal marketing strategies. The AIDA model which stands for Attention, Interest, Desire and Action which offers a conceptual framework, even with the recent implementations such as Wi-Fi advertising-based segmentation, which suffers from specific dataset usage and constrained adaptability. In this paper we have proposed a generalized AIDA-based customer segmentation framework that uses machine learning to overcome these limitations. This framework includes standardized mapping of universal availability of behavioral signals and ensures applicability across diverse domains such as e-commerce, social media, and online advertisements. We have used machine learning techniques for adaptive feature selection, hybrid clustering metrics and business-oriented measures such as conversion rate increase and customer lifetime value. This theoretical contribution extends AIDA to a flexible, cross-platform model which provides the future empirical validation.